# run setup
source("setup.R")
# load data
ww1 = readRDS(fs::path("../",controls$savepoint,"ww1.rds"))
shapes = readRDS(fs::path("../",controls$savepoint,"shapes.rds"))We use measurements of SARS-CoV-2 concentration in wastewater from multiple ARAs in Switzerland in 2022 and 2023. Viral concentration (C, unit: gene copies [gc] per liter) is transformed into viral load (V; unit: gc per day per 100,000) using the flow of wastewater on the same day (F) and the size of the population covered (P): \[ V = \frac{C \times F}{P/100,000} \] Table 1 provides a summary of the available data. Reporting frequencies and periods depended on the ARA, with daily measurements for the full period only available in a few cases (Figure 1). ARAs also sent their samples to different laboratories. In some cases, there were also changes in the method used. SARS-CoV-2 could be detected in the wastewater in most cases, with a few occurrences of no detection (Figure 2). Viral load varied over time, with large heterogeneity across ARAs, although some patterns emerge on visual inspection (Figure 3).
mw_100_desc_table(ww1) %>%
dplyr::mutate(across(everything(),as.character)) %>%
tidyr::gather() %>%
dplyr::rename(Variable=key,Value=value) %>%
flextable::flextable(cwidth=c(4,4)) Variable | Value |
|---|---|
Number of ARAs | 118 |
Number of laboratories | 9 |
Number of laboratory methods | 11 |
Number of measurements | 20535 |
Measurements below LOQ | 235 |
Measurements below LOD | 97 |
First | 2022-02-07 |
Last | 2023-05-14 |
Median viral concentration [gc/L] | 1e+05 (range: 0 to 8e+06) |
Median flow [m3/day] | 1e+04 (range: 3e+02 to 9e+05) |
Median viral load [gc/day/100,000] | 4e+12 (range: 0 to 7e+14) |
Table 1. Summary of available data.
NUTS2_name | Number of ARAs | Number of laboratories | Number of laboratory methods | Number of measurements | Measurements below LOQ | Measurements below LOD |
|---|---|---|---|---|---|---|
Central | 10 | 1 | 1 | 2,471 | 0 | 0 |
Eastern | 30 | 4 | 5 | 5,159 | 42 | 14 |
Lake Geneva | 18 | 3 | 3 | 2,302 | 2 | 5 |
Mittelland | 24 | 3 | 4 | 4,554 | 51 | 77 |
Northwest | 17 | 2 | 3 | 2,372 | 137 | 0 |
Ticino | 6 | 2 | 2 | 1,268 | 3 | 0 |
Zurich | 13 | 2 | 2 | 2,409 | 0 | 1 |
Table 2. Summary of available data by NUTS-2 region.
Figure 1. Available measurements over time by ARA (grouped by canton).
Figure 2. Total measurements by ARA.
Figure 3. SARS-CoV-2 detection in wastewater over time by ARA.
Figure 4. Daily SARS-CoV-2 viral load in wastewater by ARA (removing values below the LOD or LOQ).
Figure 5. Weekly mean SARS-CoV-2 viral load in wastewater by ARA (removing values below the LOD or LOQ). Dashed lines show the delimitation in four periods.
Figure 6. Mean SARS-CoV-2 viral load in wastewater by ARA by period.
if(FALSE) {
ggplot(ww1) +
geom_point(aes(x=pop,y=pop_total)) +
geom_abline(intercept=0,slope=1)
dd = ww1 %>%
group_by(ara_id,ara_name) %>%
summarise(pop=max(pop),
pop_total=max(pop_total)) %>%
mutate(rel=(pop_total-pop)/pop,
abs=pop-pop_total) %>%
arrange(-abs(rel))
ggplot(dd) +
geom_point(aes(x=ara_name,y=pop),colour="firebrick")+
geom_point(aes(x=ara_name,y=pop_total),colour="dodgerblue",shape=1) +
scale_x_discrete(limits=rev) +
coord_flip() +
theme(axis.text = element_text(size=5))
}